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<table width="100%"><tr><td>summary.plsone(plstools)</td><td align="right">R Documentation</td></tr></table><object type="application/x-oleobject" classid="clsid:1e2a7bd0-dab9-11d0-b93a-00c04fc99f9e">
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<h2>Summarizing Partial Least Squares regression</h2>


<h3>Description</h3>

<p>
This function provides additional elements to evaluate the goodness of fit
and the quality of the model (object of class inheriting from 'plsone').
</p>


<h3>Usage</h3>

<pre>
## S3 method for class 'plsone':
summary(object, ...)
## S3 method for class 'summary.plsone':
print(x,digits = max(3, getOption("digits") - 3),...)
</pre>


<h3>Arguments</h3>

<table summary="R argblock">
<tr valign="top"><td><code>object</code></td>
<td>
an object of class inheriting from 'plsone'.</td></tr>
<tr valign="top"><td><code>x</code></td>
<td>
an object of class inheriting from 'summary.plsone'.</td></tr>
<tr valign="top"><td><code>digits</code></td>
<td>
the number of significant digits to use when printing.</td></tr>
<tr valign="top"><td><code>...</code></td>
<td>
further arguments passed to or from other methods.</td></tr>
</table>

<h3>Details</h3>

<p>
decrire le calcul des elements ...<br>
</p>
<p>
+ additional details on the computation of each elemnts<br>
+ At the moment this function is very slow to compute the diagnostic elements.<br>
</p>
<p>
FT2 = nf * (n * n - 1) * qf(0.95, nf, n - nf)/(n * (n - nf))<br>
DcritX and  DcritY = <br>
</p>


<h3>Value</h3>

<p>
The function 'summary.plsone' retruns an list of class 'summary.plsone' containing
several elements associated with the model diagnostic:
</p>
<table summary="R argblock">
<tr valign="top"><td><code>rx2</code></td>
<td>
a matrix containing the R2 between x and th</td></tr>
<tr valign="top"><td><code>ry2</code></td>
<td>
a matrix containing the R2 between y and th</td></tr>
<tr valign="top"><td><code>T2</code></td>
<td>
a numeric vector containing Hotelling's T2 for each observation.</td></tr>
<tr valign="top"><td><code>Ts2</code></td>
<td>
a numeric vector containing adapted T2 for each observation (statistic used in SIMCA-P).</td></tr>
<tr valign="top"><td><code>FT2</code></td>
<td>
a numerical vector containing threshold to detected outlier with Hotelling's T2 (F-value).</td></tr>
<tr valign="top"><td><code>rxt2</code></td>
<td>
a matrix containing the correlations between x and th</td></tr>
<tr valign="top"><td><code>ryt2</code></td>
<td>
a matrix containing the correlations between y and th</td></tr>
<tr valign="top"><td><code>DModX</code></td>
<td>
a numeric vector containing distance from the <i>i^{th}</i> observation to the model in the space of the explanatory variables</td></tr>
<tr valign="top"><td><code>DModXN</code></td>
<td>
a numeric vector containing normaliszed values of DModX.</td></tr>
<tr valign="top"><td><code>DcritX</code></td>
<td>
a numerical vector containing threshold to detected the observations badly rebuilt by the model.</td></tr>
<tr valign="top"><td><code>DModY</code></td>
<td>
a numeric vector containing residuals of the regression of y on the 'nf' components.</td></tr>
<tr valign="top"><td><code>DModYN</code></td>
<td>
a numeric vector containing normaliszed values of DModY.</td></tr>
<tr valign="top"><td><code>DcritY</code></td>
<td>
a numerical vector containing threshold to detected the observations badly rebuilt by the model.</td></tr>
<tr valign="top"><td><code>coefficients</code></td>
<td>
a numeric vector containing regression coefficients of y on the components 'th'</td></tr>
<tr valign="top"><td><code>r.squared</code></td>
<td>
a numerical value corresponding to the R squared of linear model
('fraction of variance explained by the model',see 'summary.lm').</td></tr>
<tr valign="top"><td><code>adj.r.squared</code></td>
<td>
a numerical value corresponding to the adjusted R squared of linear model</td></tr>
<tr valign="top"><td><code>bh</code></td>
<td>
an numeric vector which contains coefficients associated with the explanatory variables</td></tr>
<tr valign="top"><td><code>n</code></td>
<td>
observation number</td></tr>
<tr valign="top"><td><code>p</code></td>
<td>
number of explanatory varaibles.</td></tr>
<tr valign="top"><td><code>nf</code></td>
<td>
an integer indicating the number of kept components (h).</td></tr>
</table>

<h3>References</h3>

<p>
Tenenhaus M. (1998) La Regression PLS. Theorie et pratique. Technip, Paris.<br>
</p>


<h3>See Also</h3>

<p>
<code><a href="plsone.html">plsone</a></code>,<code><a onclick="findlink('stats', 'summary.lm.html')" style="text-decoration: underline; color: blue; cursor: hand">summary.lm</a></code>
</p>


<h3>Examples</h3>

<pre>
require(pls)
data(yarn)
plstest1 &lt;- plsone(density ~ NIR, nf=6, data = yarn,scale=FALSE)
summary(plstest1)
summary(plstest1$lm)
</pre>

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